基于ARIMA和LSTM的城市轨道交通延时客流预测方法比较

With the transition of the development of the urban rail transit from expansion stage to operation stage in many Chinese cities,improving the operational efficiency has been considered as the development theme of the next stage.With the increasing demand for extending operation time in Chinese first-tier cities such as Beijing,Shanghai,Guangzhou and Shenzhen,how to balance the duration,cost and operational efficiency of time-extended operation of the urban rail transit has become a great challenge to refined operation.By using the data from Shanghai Metro and pre-processing the metro card data,delayed passenger flow forecast models for urban rail transit based on ARIMA and LSTM are developed.After conducting the predictive analysis for the 5 minutes intervals and 15 minute intervals by using full-day data and half-day data separately,this research finds that:1)the half-day data generally has a smaller root mean square deviation than the full-day data,which indicates that the model has a high fitting degree;2)LSTM has a smaller root mean square deviation than the ARIMA method and LSTM has a better prediction effect.The findings of this research can provide technical support for passenger flow prediction in the time extended operation of urban rail transit.